Vehicle Turn Signal Detection

ABSTRACT

Systems, methods, and devices for detecting a vehicle&#39;s turn signal status for collision avoidance during lane-switching maneuvers or otherwise. A method includes detecting, at a first vehicle, a presence of a second vehicle in an adjacent lane. The method includes identifying, in an image of the second vehicle, a sub-portion containing a turn signal indicator of the second vehicle. The method includes processing the sub-portion of the image to determine a state of the turn signal indicator. The method also includes notifying a driver or performing a driving maneuver, at the first vehicle, based on the state of the turn signal indicator.

TECHNICAL FIELD

The disclosure relates generally to methods, systems, and apparatusesfor automated driving or for assisting a driver, and more particularlyrelates to methods, systems, and apparatuses for detecting a vehicle'sturn signal status for collision avoidance during lane-switchingmaneuvers or otherwise.

BACKGROUND

Automobiles provide a significant portion of transportation forcommercial, government, and private entities. Autonomous vehicles anddriving assistance systems are currently being developed and deployed toprovide safety, reduce an amount of user input required, or eveneliminate user involvement entirely. For example, some drivingassistance systems, such as crash avoidance systems, may monitordriving, positions, and a velocity of the vehicle and other objectswhile a human is driving. When the system detects that a crash or impactis imminent the crash avoidance system may intervene and apply a brake,steer the vehicle, or perform other avoidance or safety maneuvers. Asanother example, autonomous vehicles may drive and navigate a vehiclewith little or no user input. However, due to the dangers involved indriving and the costs of vehicles, it is extremely important thatautonomous vehicles and driving assistance systems operate safely andare able to accurately navigate roads and avoid other vehicles even insituations where both autonomous vehicles and human-driven vehicles arepresent. In the case of lane-switching, merging or other road maneuversin which two vehicles, such as an autonomous vehicle and anon-autonomous vehicle, may attempt to merge into the same lane, orchange lanes near each other it is important to detect the othervehicle's status, including its turn signal status for collisionavoidance.

BRIEF DESCRIPTION OF THE DRAWINGS

Non-limiting and non-exhaustive implementations of the presentdisclosure are described with reference to the following figures,wherein like reference numerals refer to like parts throughout thevarious views unless otherwise specified. Advantages of the presentdisclosure will become better understood with regard to the followingdescription and accompanying drawings where:

FIG. 1 is a schematic block diagram illustrating an implementation of avehicle control system that includes an automated driving/assistancesystem;

FIG. 2 is a schematic diagram illustrating a top view of vehicles on amulti-lane road;

FIG. 3 illustrates a perspective view of a vehicle on a roadway;

FIG. 4 illustrates a perspective view of a vehicle on a roadway;

FIG. 5 is a schematic block diagram illustrating driving maneuverdecision making, according to one implementation;

FIG. 6 is a schematic block diagram illustrating example components of adriver intent component, according to one implementation; and

FIG. 7 is a schematic block diagram illustrating a method for driverdecision making, according to one implementation.

DETAILED DESCRIPTION

Applicants have recognized that, at least during any transition periodin which both autonomous and manually-driven vehicles will be on theroad, it is important that autonomous vehicles accurately predict theactions of human drivers. Detection of a turn signal of a vehicle usedby another driver adds certainty to the future actions of that driver inmaneuvering the vehicle. This can be useful in the case of highwaylane-switching in which two vehicles, such as an autonomous vehicle anda non-autonomous vehicle, may attempt to merge into the same lane, orchange lanes near each other. By detecting the turn signal of the othervehicle, the autonomous vehicle can detect if the other vehicle isattempting to enter its intended path and therefore avoid a possiblecollision. This detection could also be beneficial for a driver assistedvehicle that is performing a lane change or otherwise. Upon recognizingits driver's intent to change lanes (e.g., based on activation of a turnsignal on an assisted vehicle), the assisted vehicle could notify itsdriver if another vehicle is predicted to move into the same lane.

This disclosure presents systems, methods, and apparatuses forautonomous driving systems or driving assistance systems to predict ordetect lane changes of nearby vehicles. In one embodiment, a system usesthe status of an external vehicle's turn signals in order to predict themotion of that vehicle for decision-making in highway lane-changemaneuvers. The assisted vehicle can be equipped with 360 degree sensingsystem including, but not limited to, a camera, LIDAR, radar, and/orother range-finding or imaging sensors. Computer vision and sensorfusion algorithms employing deep neural networks may be trained torecognize the turn signal regions of interest on the vehicle. The neuralnetworks may also be used to identify whether the turn signal is on,possibly through comparison of the vehicle's visible turn signals, whichmay not be active all at the same time. In one embodiment, the systemmay be configured to perform the following: locate nearby vehicles thatare moving in adjacent lanes; locate and determine bounding boxes forvisible turn signals on adjacent vehicles; send image data within turnsignal bounding box to computer vision algorithm to recognize whichsignals are in use; and input turn signal status into lane-changingdecision matrix so that the system, or another system, can make a lanechange or driving maneuver decision. Even under conditions where anothervehicle has the turn signal indicators on by mistake, or a driver hasjust forgotten to turn them off, this information may be useful as anindication to pay extra attention to that vehicle and track it closelyfor potential simultaneous merges until the turn signal indicators areswitched off or the other vehicle is out of zone of possible risk.

In the following disclosure, reference is made to the accompanyingdrawings, which form a part hereof, and in which is shown by way ofillustration specific implementations in which the disclosure may bepracticed. It is understood that other implementations may be utilizedand structural changes may be made without departing from the scope ofthe present disclosure. References in the specification to “oneembodiment,” “an embodiment,” “an example embodiment,” etc., indicatethat the embodiment described may include a particular feature,structure, or characteristic, but every embodiment may not necessarilyinclude the particular feature, structure, or characteristic. Moreover,such phrases are not necessarily referring to the same embodiment.Further, when a particular feature, structure, or characteristic isdescribed in connection with an embodiment, it is submitted that it iswithin the knowledge of one skilled in the art to affect such feature,structure, or characteristic in connection with other embodimentswhether or not explicitly described.

As used herein, “autonomous vehicle” may be a vehicle that acts oroperates completely independent of a human driver; or may be a vehiclethat acts or operates independent of a human driver in some instanceswhile in other instances a human driver may be able to operate thevehicle; or may be a vehicle that is predominantly operated by a humandriver, but with the assistance of an automated driving/assistancesystem.

Implementations of the systems, devices, and methods disclosed hereinmay comprise or utilize a special purpose or general-purpose computerincluding computer hardware, such as, for example, one or moreprocessors and system memory, as discussed in greater detail below.Implementations within the scope of the present disclosure may alsoinclude physical and other computer-readable media for carrying orstoring computer-executable instructions and/or data structures. Suchcomputer-readable media can be any available media that can be accessedby a general purpose or special purpose computer system.Computer-readable media that store computer-executable instructions arecomputer storage media (devices). Computer-readable media that carrycomputer-executable instructions are transmission media. Thus, by way ofexample, and not limitation, implementations of the disclosure cancomprise at least two distinctly different kinds of computer-readablemedia: computer storage media (devices) and transmission media.

Computer storage media (devices) includes RAM, ROM, EEPROM, CD-ROM,solid state drives (“SSDs”) (e.g., based on RAM), Flash memory,phase-change memory (“PCM”), other types of memory, other optical diskstorage, magnetic disk storage or other magnetic storage devices, or anyother medium which can be used to store desired program code means inthe form of computer-executable instructions or data structures andwhich can be accessed by a general purpose or special purpose computer.

An implementation of the devices, systems, and methods disclosed hereinmay communicate over a computer network. A “network” is defined as oneor more data links that enable the transport of electronic data betweencomputer systems and/or modules and/or other electronic devices. Wheninformation is transferred or provided over a network or anothercommunications connection (either hardwired, wireless, or a combinationof hardwired or wireless) to a computer, the computer properly views theconnection as a transmission medium. Transmissions media can include anetwork and/or data links, which can be used to carry desired programcode means in the form of computer-executable instructions or datastructures and which can be accessed by a general purpose or specialpurpose computer. Combinations of the above should also be includedwithin the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions anddata which, when executed at a processor, cause a general purposecomputer, special purpose computer, or special purpose processing deviceto perform a certain function or group of functions. The computerexecutable instructions may be, for example, binaries, intermediateformat instructions such as assembly language, or even source code.Although the subject matter has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the subject matter defined in the appended claims is notnecessarily limited to the described features or acts described above.Rather, the described features and acts are disclosed as example formsof implementing the claims.

Those skilled in the art will appreciate that the disclosure may bepracticed in network computing environments with many types of computersystem configurations, including, an in-dash computer, personalcomputers, desktop computers, laptop computers, message processors,hand-held devices, multi-processor systems, microprocessor-based orprogrammable consumer electronics, network PCs, minicomputers, mainframecomputers, mobile telephones, PDAs, tablets, pagers, routers, switches,various storage devices, and the like. The disclosure may also bepracticed in distributed system environments where local and remotecomputer systems, which are linked (either by hardwired data links,wireless data links, or by a combination of hardwired and wireless datalinks) through a network, both perform tasks. In a distributed systemenvironment, program modules may be located in both local and remotememory storage devices.

Further, where appropriate, functions described herein can be performedin one or more of: hardware, software, firmware, digital components, oranalog components. For example, one or more application specificintegrated circuits (ASICs) can be programmed to carry out one or moreof the systems and procedures described herein. Certain terms are usedthroughout the following description and claims to refer to particularsystem components. As one skilled in the art will appreciate, componentsmay be referred to by different names. This document does not intend todistinguish between components that differ in name, but not function.

Referring now to the figures, FIG. 1 illustrates a vehicle controlsystem 100 that includes an automated driving/assistance system 102. Theautomated driving/assistance system 102 may be used to automate, assist,or control operation of a vehicle, such as a car, truck, van, bus, largetruck, emergency vehicles or any other automobile for transportingpeople or goods, or to provide assistance to a human driver. Forexample, the automated driving/assistance system 102 may control one ormore of braking, steering, acceleration, lights, alerts, drivernotifications, radio, or any other auxiliary systems of the vehicle. Inanother example, the automated driving/assistance system 102 may not beable to provide any control of the driving (e.g., steering,acceleration, or braking), but may provide notifications and alerts toassist a human driver in driving safely. The automateddriving/assistance system 102 includes a driver intent component 104,which may predict a future movement of other vehicles based on one ormore of turn signal indicators and vehicle movement. For example, thedriver intent component 104 may estimate an intention of the driver of adifferent vehicle (e.g., a vehicle that does not include the vehiclecontrol system 100) based on a turn signal state of the differentvehicle.

The vehicle control system 100 also includes one or more sensorsystems/devices for detecting a presence of nearby objects ordetermining a location of a parent vehicle (e.g., a vehicle thatincludes the vehicle control system 100) or nearby objects. For example,the vehicle control system 100 may include one or more radar systems106, one or more LIDAR systems 108, one or more camera systems 110, aglobal positioning system (GPS) 112, and/or one or more ultrasoundsystems 114. The vehicle control system 100 may include a data store 116for storing relevant or useful data for navigation and safety such asmap data, driving history or other data. The vehicle control system 100may also include a transceiver 118 for wireless communication with amobile or wireless network, other vehicles, infrastructure, or any othercommunication system. The vehicle control system 100 may include vehiclecontrol actuators 120 to control various aspects of the driving of thevehicle such as electric motors, switches or other actuators, to controlbraking, acceleration, steering or the like. The vehicle control system100 may also include one or more displays 122, speakers 124, or otherdevices so that notifications to a human driver or passenger may beprovided. The display 122 may include a heads-up display, a dashboarddisplay or indicator, a display screen, or any other visual indicator,which may be seen by a driver or passenger of a vehicle. The speakers124 may include one or more speakers of a sound system of a vehicle ormay include a speaker dedicated to driver notification.

It will be appreciated that the embodiment of FIG. 1 is given by way ofexample only. Other embodiments may include fewer or additionalcomponents without departing from the scope of the disclosure.Additionally, illustrated components may be combined or included withinother components without limitation. For example, the driver intentcomponent 104 may be separate from the automated driving/assistancesystem 102 and the data store 116 may be included as part of theautomated driving/assistance system 102 and/or part of the driver intentcomponent 104.

The radar system 106 may include any radar system known in the art. Ingeneral, a radar system 106 operates by transmitting radio signals anddetecting reflections off objects. In ground applications, the radar maybe used to detect physical objects, such as other vehicles, parkingbarriers or parking chocks, landscapes (such as trees, cliffs, rocks,hills, or the like), road edges, signs, buildings, or other objects. Theradar system 106 may use the reflected radio waves to determine a size,shape, distance, surface texture, or other information about a physicalobject or material. For example, the radar system 106 may sweep an areato obtain data about objects within a specific range and viewing angleof the radar system 106. In one embodiment, the radar system 106 isconfigured to generate perception information from a region near thevehicle, such as one or more regions nearby or surrounding the vehicle.For example, the radar system 106 may obtain data about regions of theground or vertical area immediately neighboring or near the vehicle. Theradar system 106 may include one of many widely available commerciallyavailable radar systems. In one embodiment, the radar system 106 mayprovide perception data including a two dimensional or three-dimensionalmap or model to the automated driving/assistance system 102 forreference or processing.

The LIDAR system 108 may include any LIDAR system in the art. Ingeneral, the LIDAR system 108 operates by emitting visible wavelength orinfrared wavelength lasers and detecting reflections of the laser lightoff objects. In ground applications, the lasers may be used to detectphysical objects, such as other vehicles, parking barriers or parkingchocks, landscapes (such as trees, cliffs, rocks, hills, or the like),road edges, signs, buildings, or other objects. The LIDAR system 108 mayuse the reflected laser light to determine a size, shape, distance,surface texture, or other information about a physical object ormaterial. For example, the LIDAR system 108 may sweep an area to obtaindata or objects within a specific range and viewing angle of the LIDARsystem 108. For example, the LIDAR system 108 may obtain data aboutregions of the ground or vertical area immediately neighboring or nearthe vehicle. The LIDAR system 108 may include one of many widelyavailable commercially available LIDAR systems. In one embodiment, theLIDAR system 108 may provide perception data including a two dimensionalor three-dimensional model or map of detected objects or surfaces.

The camera system 110 may include one or more cameras, such as visiblewavelength cameras or infrared cameras. The camera system 110 mayprovide a video feed or periodic images, which can be processed forobject detection, road identification and positioning, or otherdetection or positioning. In one embodiment, the camera system 110 mayinclude two or more cameras, which may be used to provide ranging (e.g.,detecting a distance) for objects within view. In one embodiment, imageprocessing may be used on captured camera images or video to detectvehicles, turn signals, drivers, gestures, and/or body language of adriver. In one embodiment, the camera system 110 may include camerasthat obtain images for two or more directions around the vehicle.

The GPS system 112 is one embodiment of a positioning system that mayprovide a geographical location of the vehicle based on satellite orradio tower signals. GPS systems 112 are well known and widely availablein the art. Although GPS systems 112 can provide very accuratepositioning information, GPS systems 112 generally provide little or noinformation about distances between the vehicle and other objects.Rather, they simply provide a location, which can then be compared withother data, such as maps, to determine distances to other objects,roads, or locations of interest.

The ultrasound system 114 may be used to detect objects or distancesbetween a vehicle and objects using ultrasonic waves. For example, theultrasound system 114 may emit ultrasonic waves from a location on ornear a bumper or side panel location of a vehicle. The ultrasonic waves,which can travel short distances through air, may reflect off otherobjects and be detected by the ultrasound system 114. Based on an amountof time between emission and reception of reflected ultrasonic waves,the ultrasound system 114 may be able to detect accurate distancesbetween a bumper or side panel and any other objects. Due to its shorterrange, ultrasound systems 114 may be more useful to detect objectsduring parking or to detect imminent collisions during driving.

In one embodiment, the radar system(s) 106, the LIDAR system(s) 108, thecamera system(s) 110, and the ultrasound system(s) 114 may detectenvironmental attributers or obstacles near a vehicle. For example, thesystems 106-110 and 114 may detect other vehicles, pedestrians, people,animals, a number of lanes, lane width, shoulder width, road surfacecurvature, road direction curvature, rumble strips, lane markings,presence of intersections, road signs, bridges, overpasses, barriers,medians, curbs, or any other details about a road. As a further example,the systems 106-110 and 114 may detect environmental attributes thatinclude information about structures, objects, or surfaces near theroad, such as the presence of drive ways, parking lots, parking lotexits/entrances, sidewalks, walkways, trees, fences, buildings, parkedvehicles (on or near the road), gates, signs, parking strips, or anyother structures or objects.

The data store 116 stores map data, driving history, and other data,which may include other navigational data, settings, or operatinginstructions for the automated driving/assistance system 102. The mapdata may include location data, such as GPS location data, for roads,parking lots, parking stalls, or other places where a vehicle may bedriven or parked. For example, the location data for roads may includelocation data for specific lanes, such as lane direction, merging lanes,highway or freeway lanes, exit lanes, or any other lane or division of aroad. The location data may also include locations for one or moreparking stall in a parking lot or for parking stalls along a road. Inone embodiment, the map data includes location data about one or morestructures or objects on or near the roads or parking locations. Forexample, the map data may include data regarding GPS sign location,bridge location, building or other structure location, or the like. Inone embodiment, the map data may include precise location data withaccuracy within a few meters or within sub meter accuracy. The map datamay also include location data for paths, dirt roads, or other roads orpaths, which may be driven by a land vehicle.

The transceiver 118 is configured to receive signals from one or moreother data or signal sources. The transceiver 118 may include one ormore radios configured to communicate according to a variety ofcommunication standards and/or using a variety of different frequencies.For example, the transceiver 118 may receive signals from othervehicles. Receiving signals from another vehicle is referenced herein asvehicle-to-vehicle (V2V) communication. In one embodiment, thetransceiver 118 may also be used to transmit information to othervehicles to potentially assist them in locating vehicles or objects.During V2V communication the transceiver 118 may receive informationfrom other vehicles about their locations, previous locations or states,other traffic, accidents, road conditions, the locations of parkingbarriers or parking chocks, or any other details that may assist thevehicle and/or automated driving/assistance system 102 in drivingaccurately or safely. For example, the transceiver 118 may receiveupdated models or algorithms for use by a driver intent component 104 indetecting vehicle movement, turn signals, or body language of a driverof another vehicle.

The transceiver 118 may receive signals from other signal sources thatare at fixed locations. Infrastructure transceivers may be located at aspecific geographic location and may transmit its specific geographiclocation with a time stamp. Thus, the automated driving/assistancesystem 102 may be able to determine a distance from the infrastructuretransceivers based on the time stamp and then determine its locationbased on the location of the infrastructure transceivers. In oneembodiment, receiving or sending location data from devices or towers atfixed locations is referenced herein as vehicle-to-infrastructure (V2X)communication. V2X communication may also be used to provide informationabout locations of other vehicles, their previous states, or the like.For example, V2X communications may include information about how long avehicle has been stopped or waiting at an intersection, highway on-rampsignal, or the like. In one embodiment, the term V2X communication mayalso encompass V2V communication.

In one embodiment, the automated driving/assistance system 102 isconfigured to control driving or navigation of a parent vehicle. Forexample, the automated driving/assistance system 102 may control thevehicle control actuators 120 to drive a path on a road, parking lot,through an intersection, driveway or other location. For example, theautomated driving/assistance system 102 may determine a path and speedto drive based on information or perception data provided by any of thecomponents 106-118. As another example, the automated driving/assistancesystem 102 may determine when to change lanes, merge, or when to leavespace for another vehicle to change lanes, or the like.

In one embodiment, the driver intent component 104 is configured todetermine an intent of a driver of a nearby vehicle and/or predict afuture movement, and timing for the movement, of a vehicle under controlof a human driver. For example, the driver intent component 104 isconfigured to determine whether or not to change lanes or to determinethe timing to change lanes.

FIG. 2 is a schematic top view of a roadway 200 with a vehicle 202traveling on the roadway. The vehicle 202 is traveling in a first lane208 of the roadway 200. The roadway also includes a second lane 210 anda third lane 212 that have a same direction of travel as the first lane208. Additional vehicles 204 and 206 are also traveling along theroadway 200 in the third lane 212. The vehicle 202 may include thesystem 100 of FIG. 1. In one embodiment, one or more sensors, such asthe camera system 110, may gather perception data of the road 200 andregions around the vehicle 202. A viewing area of the camera system 110,the LIDAR system 108, the radar system 106 or other system may extend inany direction or all directions around the vehicle 202. The vehicle 202,or a driver intent component 104 of the vehicle 202, may receiveperception data from the sensors and detect the presence of othervehicles, objects, surfaces, or the like within a viewing range of thevehicle 202. For example, the vehicle 202 may detect and identifyadditional vehicles 204 and 206 as nearby vehicles.

In one embodiment, the vehicle 202 may detect a presence of theadditional vehicles 204 and 206 in other lanes of the roadway 200. Inone embodiment, the vehicle 202 may capture one or more images of theadditional vehicles and identify a sub-portion of the captured imagesthat corresponds to locations of turn signals on the other vehicles thatare visible in the images. For example, the vehicle 202 may process thecaptured images using a neural network to identify regions that arelikely to correspond to a location of turn signals. The vehicle 202 mayanalyze or process the sub-portion of the perception data (or image) todetermine a state of a turn signal. Based on the state of the turnsignal, the vehicle 202 may notify a driver to delay changing lanes intothe second lane 210 or may cause an autonomous driving system to delaychanging lanes into the second lane 210.

The vehicle control system 100 of FIG. 1 may help warn or prevent thevehicle 202 from merging into a lane at the same time as anothervehicle, or may warn a driver or cause the vehicle 202 to slow down orspeed up to allow another vehicle to merge into the vehicle's 202current lane, such as 208. Notifications and determinations thatvehicles are changing lanes may help reduce accidents between movingvehicles traveling in a same or similar direction along a roadway. Forexample, the vehicle 202 may detect when the additional vehicle 204 hasa left turn signal on and thus the vehicle 202 should wait or avoidchanging lanes into the second lane 210. If the additional vehicle 204were already in the second lane 210, then the vehicle 202 may detect theleft turn signal of vehicle 204 and inform the driver (or an automateddriving system) that the vehicle 202 should slow down to let theadditional vehicle 204 to enter into the first lane 208 in front of thevehicle 202. As another example, the vehicle 202 may detect that theadditional vehicle 206 is changing from the third lane 212 into thesecond lane 210. If the additional vehicle 206 is within the second lane210 with a left turn signal on, then the vehicle 202 may speed up toallow the additional vehicle 206 to enter into the first lane 208 behindthe vehicle 202.

In addition to turn signal states, the vehicle control system 100 maydetermine one or more details about movement of the additional vehicles204 and 206. For example, the vehicle control system 100 may detect arelative speed, direction, or other movement of the additional vehicles204 and 206. These movement details may further inform whether a lanechange, slow down, acceleration, or other driving maneuver should beperformed by the vehicle 202 to avoid collision or maintain safe drivingdistances between vehicles.

FIG. 3 illustrates a perspective view of an image 300 that may becaptured by a camera of a vehicle control system 100. The image 300shows a rear side of a vehicle 302. A sub-portion 304 of the imageincludes turn signals of the vehicle 302. In one embodiment, the vehiclecontrol system 100 may identify the sub-region 304 as a region to beprocessed for determining states of turn signals. For example, thevehicle control system 100 may generate a bounding box including thesub-region 304 and feed image content within the bounding box into aneural network that has been trained to detect turn signal states. Basedon the state of the turn signal indicators, the vehicle control system100 may determine that the vehicle 302 is about to change lanes or mayleave a distance to allow the vehicle 302 to change lanes. Even if aturn signal has accidentally been turned on or left on, it may bebeneficial if the vehicle control system 100 leaves some room for thevehicle 302. For example, an inadvertent turn signal may indicate thatthe vehicle 302 movements may be unpredictable due to an inattentivehuman operator or a software error of an automated driving system.

FIG. 4 illustrates another perspective view of an image 400 that may becaptured by a camera of a vehicle control system 100. The image 400shows a front left view of the vehicle 402. For example, a rearward orsideways facing camera on a vehicle may capture the image 400 when thevehicle 402 is behind and to the right of a parent vehicle. In oneembodiment, the vehicle control system 100 may identify a firstsub-region 404 and a second sub-region 406 as regions to be processedfor determining states of turn signals. For example, the vehicle controlsystem 100 may generate a bounding box, including the sub-regions 404and 406, and feed image content within the bounding boxes into a neuralnetwork that has been trained to detect turn signal states.

In the image 400, only the second sub-region 406 includes a turn signal,specifically the left turn signal 408 of the vehicle 402. A right turnsignal of the vehicle 402 is out of view and is not actually visible inthe first sub-region 404. Thus, only the state of one of the turnsignals may be detected, namely the left turn signal 408. The vehiclecontrol system 100 may detect the state of the left turn signal 408 anddetermine that no other turn signals are detectable. Based on the stateof the left turn signal 408, the vehicle control system 100 may be ableto predict one or more actions of the vehicle 402. For example, if theleft turn signal 408 is blinking, the vehicle control system 100 maydetermine that the vehicle 402 is either preparing for a switch into alane to the left of the vehicle 402 or may determine that the vehicle402 has hazard lights flashing and thus a driver of the vehicle may bein a hurry and space should be provided to allow the vehicle 402 room tonavigate. On the other hand, if the left turn signal 408 is notblinking, the vehicle control system 100 may determine that the vehicle402 will likely remain in the same lane or move to a lane to the rightof the vehicle. Because staying in the same lane or moving to the rightmay indicate that the vehicle 402 is not likely to make movements thatwould be threatening or unsafe to the vehicle that captured the image400.

FIG. 5 is a schematic block diagram illustrating a method 500 fordeciding on a driving maneuver. Perception data, such as camera data,LIDAR data, radar data and ultrasound data, is obtained at 502 and adriver intent component 104 identifies and localizes a vehicle based onthe perception data at 504. For example, the driver intent component 104may identify a region of a viewing area or a region of an image thatcorresponds to a vehicle and may localize that vehicle in a same oradjacent lane. For example, the driver intent component 104 may identifya lane of the other vehicle with respect to a parent vehicle of thedriver intent component 104. The adjacent lane may include animmediately adjacent lane (e.g., to a right or left of a current lane ofa vehicle) or may include a lane offset by one or more lanes from thevehicle (e.g., two lanes to the right or left of the current lane). Thedriver intent component 104 finds a region of interest at 506 in animage in the camera data that includes or likely includes a visible turnsignal of a localized vehicle. For example, the driver intent component104 may feed an image captured by a camera into a neural network thatidentifies regions including, or likely including, vehicle turn signals.

The region of interest may include a region near a front or rear bumper,a headlight, and/or a tail light. The driver intent component 104 alsodetermines a turn signal state or identifies the status of visible turnsignals at 508. For example, the driver intent component 104 maydetermine whether a turn signal indicator is off or blinking at 508.Furthermore, the driver intent component 104 may determine an identityof a visible turn signal as a left front, right front, left rear, and/orright rear turn signal. Furthermore, the driver intent component 104 maydetermine an identity of turn signals that are not visible (e.g., leftfront, right front, left rear, and/or right rear turn signal). Thedriver intent component 104 may then provide the turn signal status,and/or turn signal identity, to a decision matrix at 510. The decisionmatrix may include a plurality of values that may be considered indetermining a future maneuver of a vehicle. For example, the matrix maybe used to determine whether to change a lane, speed up, slow down, orperform any other maneuver or combination of maneuvers. In oneembodiment, the matrix may be used to determine what notifications toprovide to a driver of a vehicle. For example, possible notificationsmay include blind spot warnings, lane change instructions, or any othernotifications or alerts to a human driver.

FIG. 6 is a schematic block diagram illustrating components of a driverintent component 104, according to one embodiment. The driver intentcomponent 104 includes a perception data component 602, a detectioncomponent 604, a boundary component 606, a turn signal component 608, avehicle movement component 610, a prediction component 612, a drivingmaneuver component 614, and a notification component 616. The components602-616 are given by way of illustration only and may not all beincluded in all embodiments. In fact, some embodiments may include onlyone or any combination of two or more of the components 602-616. Some ofthe components 602-616 may be located outside the driver intentcomponent 104, such as within the automated driving/assistance system102 or elsewhere.

The perception data component 602 is configured to receive sensor datafrom one or more sensor systems of the vehicle. For example, theperception data component 602 may receive data from the radar system106, the LIDAR system 108, the camera system 110, the GPS 112, theultrasound system 114, or the like. In one embodiment, the perceptiondata may include perception data for one or more regions near thevehicle. For example, sensors of the vehicle may provide a 360 degreeview around the vehicle. In one embodiment, the camera system 110captures an image of a vehicle. For example, the captured image of thevehicle may be proximal to a parent vehicle of the driver intentcomponent 104. In one embodiment, the camera system 110 captures animage of a proximal vehicle that is in a same lane or in a lane near thevehicle and traveling in the same direction as the parent vehicle.

The detection component 604 is configured to detect a presence of one ormore nearby vehicles. In one embodiment, the detection component 604detects the nearby vehicles based on perception data gathered by theperception data component 602. For example, the detection component 604may detect a moving object about the size of the vehicle, or may useobject recognition on images obtained by a camera system 110 to detectthe vehicle. In one embodiment, the detection component 604 may detectlane lines, or the like, to detect lanes or other physical features tolocalize vehicles with respect to a parent vehicle.

In one embodiment, the detection component 604 is configured todetermine a lane of one or more of the vehicle and the proximal vehicle.For example, the detection component 604 may determine that a parentvehicle is in a first lane and that a detected proximal vehicle is in asecond lane or a third lane neighboring the first lane. In oneembodiment, the detection component 604 is configured to determinewhether a proximal vehicle is in an adjacent lane to a current lane ofthe parent vehicle. The adjacent lane may be a lane immediately adjacenta current lane or may be offset by one or more intervening lanes.

In one embodiment, the detection component 604 may determine whether adetected vehicle is within a risk zone of the parent vehicle. The riskzone may be a region within a threshold distance of the parent vehicle.In one embodiment, the threshold distance may vary based on a speed orcurrent road conditions. The risk zone may include an area where thereis a risk of collision between a parent vehicle and a detected vehicle.In one embodiment, the detection component 604 may also determinewhether the vehicle is moving in the same or similar direction as aparent vehicle. For example, the detection component 604 may determinewhether the proximal vehicle is traveling in the same direction alongthe roadway as the parent vehicle.

The boundary component 606 is configured to identify a sub-region ofperception data that corresponds to, or likely corresponds to, alocation of a turn signal. In one embodiment, the boundary component 606is configured to locate one or more vehicles within images or otherperception data. For example, object recognition algorithms may be usedto identify detected objects or obstacles as vehicles. In oneembodiment, the boundary component 606 may identify a boundary of thevehicle and identify pixels or objects in that region as correspondingto the vehicle. Edge or boundary finding image processing algorithms maybe used to find the edges of the vehicle.

In one embodiment, the boundary component 606 is configured to identifya sub-portion of an image (or multiple images) that contains a turnsignal indicator of a vehicle. For example, the sub-portion of the imagemay include a turn signal indicator light positioned on a front, rear,or any other location of a vehicle. In one embodiment, the sub-portionmay include a region on or near a bumper, and/or a region near aheadlight or tail light of the vehicle. In one embodiment, the boundarycomponent 606 may identify the sub-portion of the image that containsthe turn signal using object recognition or edge detection imageprocessing algorithms. For example, the boundary component 606 mayidentify an edge or boundary of an indicator light, headlight, taillight, or the like. In one embodiment, the boundary component 606 isconfigured to identify the sub-portion containing the turn signalindicator by processing the image of the proximal vehicle using a neuralnetwork trained to recognize one or more turn signal regions ofinterest. In one embodiment, the boundary component 606 may determine aregion surrounding the turn signal indicator or a region larger than theturn signal indicator so that states of the turn signal indicator may beaccurately determined even if the boundary is not perfectly aligned orcentered on the turn-signal indicator.

The turn signal component 608 is configured to determine a state of aturn signal of a proximal vehicle. For example, the turn signalcomponent 608 may determine a state of one or more turn signals of avehicle that is located in a same or adjacent lane near a parentvehicle. In one embodiment, the turn signal component 608 may processone or more sub-regions determined by the boundary component 606 todetermine the state of the turn signal indicator. For example, the turnsignal component 608 may detect whether a turn signal indicator isemitting light or blinking based on one or more images of the turnsignal indicator. In one embodiment, the turn signal component 608 isconfigured to process the sub-portion of the image using a neuralnetwork trained to determine a state of one or more turn signalindicators.

In one embodiment, the turn signal component 608 may determine whetherone or more of a left turn signal indicator and a right turn indicatorare on or blinking. For example, the turn signal indicator 608 maydetermine whether only the left turn signal indicator is flashing, onlythe right turn signal indicator is flashing, both the left turn signalindicator and the right turn signal indicator are flashing, or neitherthe left turn signal indicator nor the right turn signal indicator areflashing. In one embodiment, the turn signal component 608 may alsodetermine whether some of the turn signal indicators of a vehicle arenot visible in an image. For example, the turn signal component 608 maydetermine that a specific turn signal indicator has an unknown statebecause it is not visible, or is unknown for some other reason.

The vehicle movement component 610 is configured to detect one or moremovements of a proximal vehicle. For example, the vehicle movementcomponent 610 may detect the movements of the proximal vehicle based onperception data or other sensor data received by the perception datacomponent 602. In one embodiment, the vehicle movement component 610 maydetermine one or more accelerations, decelerations, turns, or the likeof a proximal vehicle. In one embodiment, the vehicle movement component610 may detect that a vehicle is traveling in a same or similardirection as a parent vehicle. For example, the vehicle movementcomponent 610 may determine that the proximal vehicle is traveling alonga roadway in a same direction, even if the vehicle is in the same ordifferent lane as the parent vehicle.

The boundary component 606, turn signal component 608, and/or thevehicle movement component 610 may include models, neural networks,machine learned parameters, or the like to detect body language, turnsignal states, and vehicle movements. For example, guided or unguidedmachine learning algorithms may process perception data from real-worldor virtual environments to learn shapes, movements, or other imagecontent that corresponds to body language, turn signal states, orvehicle movement. The results of these machine learning algorithms maybe included in models or databases for use by respective components todetect the body language, turn signal states, or vehicle movement duringdriving of a vehicle.

The prediction component 612 may infer a driver's intention or predictfuture motions of a nearby vehicle based determinations by the turnsignal component 608, the vehicle movement component 610, and/or otherinformation. For example, the prediction component 612 may predictfuture motion, and/or a timing for the future motion, based on a stateof a turn signal indicator and/or detected vehicle movements. In oneembodiment, the prediction component 612 determines the driver'sintention or predicts future motions. For example, the predictioncomponent 612 may determine a timing and a movement that the driverintends to perform. Example movements may include a turn onto adifferent road, waiting at an intersection, merging with traffic,changing lanes, exiting a roadway, entering a roadway, parking avehicle, exiting a parking spot, or the like.

In one embodiment, the prediction component 612 references or processesa database or model to determine a predicted movement or intendedmovement of another vehicle. For example, the prediction component 612may use a neural network that has been trained to determine a futurevehicle movement based on detected vehicle movements and/or the statusof a turn signal. In one embodiment, the database or model alsocorrelates an intention of a driver or a future driving maneuver basedon a current driving context. For example, the same gestures may meandifferent things based on whether the nearby vehicle or parent vehicleis stopped at an intersection, approaching an intersection, driving downa road with one or more nearby vehicles, merging onto a roadway, exitinga roadway, entering a parking lot or parking spot, exiting a parking lotor parking spot, or the like. Thus, gestures and current driving contextmay be used to accurately infer an intention of a driver or predict afuture driving maneuver. The prediction component 612 may provide thepredicted driving maneuvers or driver intent to the driving maneuvercomponent 614 or the automated driving/assistance system 102 fordecision making and for maneuvers or actions to be taken by theautomated driving/assistance system 102 or a parent vehicle.

In one embodiment, the prediction component 612 determines the driver'sintention or predicts future motions of the vehicle based on a state ofa turn signal indicator as determined by the turn signal component 608.For example, the prediction component 612 may predict a timing anddirection of travel for the vehicle to change lanes, merge, or exit aroadway. In one embodiment, the prediction component 612 references orprocesses the data based on a neural network to determine the predictedmovement or intended movement of another vehicle. For example, theprediction component 612 may include or access a database or model thatcorrelates turn signal statuses with one or more future vehiclemovements. In one embodiment, the database or model may correlate ablinker direction with the vehicle changing direction in that direction.In one embodiment, the database or model may correlate a flashing ofhazard lights (e.g., both or all detectable turn signal indicatorsblinking) with a vehicle moving quickly down a roadway in anunpredictable direction without waiting according to normal protocols.Thus, extra distance and leeway may be provided to the vehicle withflashing hazard lights (or flashing emergency lights) to move down theroadway.

In one embodiment, the prediction component 612 determines the driver'sintention or predicts future motions based on detected movements of thevehicle as determined by the vehicle movement component 610. Forexample, the prediction component 612 may predict a timing and directionof travel for the vehicle to move through the intersection. In oneembodiment, the prediction component 612 references or processes thedatabase or model to determine the predicted movement or intendedmovement of another vehicle. For example, the prediction component 612may include or access a database or model that correlates one or moredetected movements with one or more future movements. For example, thedatabase may include an acceleration, velocity, deceleration, or othermovement information with a predicted further movement through anintersection. In one embodiment, the prediction component 612 maypredict a lane change from the adjacent lane into a same lane as thefirst vehicle. In one embodiment, the prediction component 612 maypredict that a proximal vehicle may change lanes in directioncorresponding to a side of the vehicle on which a turn signal indicatoris active or blinking.

In one embodiment, the prediction component 612 may determine a driver'sintention or future movement of a vehicle based on a combination of datafrom the turn signal component 608, vehicle movement component 610, orother data. For example, a neural network or graphical model may includemachine learning values or correlations for one or more of turn signalinformation and vehicle movements.

The driving maneuver component 614 is configured to select a drivingmaneuver for a parent vehicle based on the predicted driver intent orfuture driving maneuver of another vehicle. For example, the drivingmaneuver component 614 may receive one or more predicted drivingmaneuvers for one or more nearby vehicles from the prediction component612. The driving maneuver may determine a driving path to avoidcollision with the other vehicles in case they perform the predicteddriving maneuvers. For example, the driving maneuver component 614 maydetermine whether to decelerate, accelerate, and/or turn a steeringwheel of the parent vehicle. In one embodiment, the driving maneuvercomponent 614 may determine a timing for the driving maneuver. Forexample, the driving maneuver component 614 may determine that a parentvehicle should wait to perform a lane change or perform a lane change ata specific time because another vehicle is likely to be near the vehiclein that lane.

In one embodiment, the driving maneuver component 614 may select adriving maneuver based directly on data gathered by the turn signalcomponent 608, vehicle movement component 610, and/or other componentsof the driver intent component 104. For example, the driving maneuvercomponent 614 may select a driving maneuver based on a state of a turnsignal indicator and/or a location and velocity of another vehicle. Inone embodiment, a selected driving maneuver may be a suggested drivingmaneuver that is provided to a driver or a system that makes driving ornavigation maneuvers. In one embodiment, the driving maneuver component614 may enter or include data or determinations from the turn signalcomponent 608, vehicle movement component 610, and/or the predictioncomponent 612 in a decision matrix. For example, the decision matrix mayinclude a matrix that is processed using a neural network or aprocessing algorithm to determine a maneuver the parent vehicle shouldperform. In one embodiment, the decision matrix may include a matrix fordeciding whether and/or when to perform a lane change.

The notification component 616 is configured to provide one or morenotifications to a driver or automated driving system of a vehicle. Inone embodiment, the notification component 616 may provide notificationsto a driver using a display 122 or speaker 124. In one embodiment, thenotification may include an instruction to perform a maneuver or maywarn that another vehicle is likely to perform a specific maneuver or beat a specific location at a predicted time. In one embodiment, thenotification component 616 may notify the driver or automated drivingsystem of a driving maneuver selected or suggested by the drivingmaneuver component 614. In one embodiment, the notification component616 may notify the driver or automated driving system of a predictedfuture movement of another vehicle as determined by the predictioncomponent 612.

Referring now to FIG. 7, a schematic flow chart diagram of a method 700for vehicle maneuver decision making, according to one embodiment, isillustrated. The method 700 may be performed by an automateddriving/assistance system or a driver intent component, such as theautomated driving/assistance system 102 of FIG. 1 or the driver intentcomponent 104 of FIG. 1 or 6.

The method 700 begins as a boundary component 606 identifies, in animage of a proximal vehicle, a sub-portion containing a turn signalindicator of the proximal vehicle at 702. A turn signal component 608processes the sub-portion of the image to determine a state of the turnsignal indicator at 704. A notification component 616 notifies a driveror a driving maneuver component 614 causes a vehicle to perform adriving maneuver based on the state of the turn signal indicator of theproximal vehicle at 706.

EXAMPLES

The following examples pertain to further embodiments.

Example 1 is a method that includes detecting, at a first vehicle, apresence of a second vehicle in an adjacent lane. The method alsoincludes identifying, in an image of the second vehicle, a sub-portioncontaining a turn signal indicator of the second vehicle. The methodfurther includes processing the sub-portion of the image to determine astate of the turn signal indicator. The method also includes notifying adriver or performing a driving maneuver, at the first vehicle, based onthe state of the turn signal indicator.

In Example 2, identifying the sub-portion containing the turn signalindicator in Example 1 includes processing the image of the secondvehicle using a neural network trained to recognize one or more turnsignal regions of interest. Furthermore, processing the sub-portion ofthe image includes processing a portion of the image comprising the oneor more turn signal regions of interest.

In Example 3, processing the sub-portion of the image in any of Examples1-2 includes processing the sub-portion using a neural network trainedto determine a state of one or more turn signal indicators.

In Example 4, the method of any of Examples 1-3 further includesdetermining the driving maneuver for the first vehicle based on thestate of the turn signal indicator.

In Example 5, determining the driving maneuver in Example 4 includesproviding the state of the turn signal indicator into a lane-changingdecision matrix and processing the lane-changing decision matrix toselect the driving maneuver.

In Example 6, notifying the driver in Example 4 includes notifying thedriver of the determined driving maneuver.

In Example 7, the method of any of Examples 1-6 further includespredicting future movement of the second vehicle based on a state of theturn signal indicator.

In Example 8, predicting future movement in Example 7 includespredicting a lane change from the adjacent lane into a same lane as thefirst vehicle.

In Example 9, notifying the driver in Example 7 includes notifying thedriver of the predicted future movement of the second vehicle.

In Example 10, the method of any of Examples 1-9 further includedetermining a direction of movement of the first vehicle correspondingto the state of the turn signal indicator.

Example 11 is a driving control system for a vehicle that includes oneor more sensors for obtaining sensor data in a region near a vehiclethat include a camera. The driving control system also includes aboundary component, a turn signal component, and a driving maneuvercomponent. The boundary component is configured to identify asub-portion of an image containing a turn signal indicator of a proximalvehicle. The turn signal component is configured to process thesub-portion of the image to determine a state of the turn signalindicator. The driving maneuver component is configured to determine adriving maneuver for the first vehicle based on the state of the turnsignal indicator.

In Example 12, the driving maneuver in Example 12 includes a suggesteddriving maneuver and the driving control system further includes anotification component configured to provide the suggested drivingmaneuver to a human driver or an automated driving system.

In Example 13, the boundary component in any of Examples 11-12 isconfigured to identify the sub-portion containing the turn signalindicator by processing the image of the proximal vehicle using a neuralnetwork trained to recognize one or more turn signal regions ofinterest. The turn signal component is configured to process thesub-portion of the image by processing a portion of the image comprisingthe one or more turn signal regions of interest.

In Example 14, the driving control system of any of Examples 11-13further includes a detection component configured to determine that theproximal vehicle is within a risk zone with respect to the vehicle. Thedriving control system is configured to obtain the image in response tothe detection component determining that the proximal vehicle is withinthe risk zone.

In Example 15, the driving control system of any of Examples 11-14further includes a detection component configured to determine a lane ofone or more of the vehicle and the proximal vehicle.

Example 16 is a computer readable storage media storing instructionsthat, when executed by one or more processors, cause the one or moreprocessors to obtain, at a first vehicle, a plurality of images of asecond vehicle on a road near the first vehicle. The instructionsfurther cause the one or more processors to identify a sub-portion ofthe plurality of images containing a turn signal indicator of the secondvehicle. The instructions further cause the one or more processors toprocess the sub-portion of the plurality of images to determine a stateof the turn signal indicator. The instructions further cause the one ormore processors to predict a driving maneuver for the second vehiclebased on the state of the turn signal indicator.

In Example 17, the instructions of Example 16 further cause the one ormore processors to determine a driving maneuver for the first vehiclebased on the predicted driving maneuver for the second vehicle.

In Example 18, the instructions in any of Examples 16-17 further causethe one or more processors to determine whether or not to cause thefirst vehicle to perform a lane change based on the turn signalindicator.

In Example 19, the instructions in any of Examples 16-18 further causethe one or more processors to determine that the second vehicle is oneor more of: in a lane immediately adjacent a lane of the first vehicle;driving in a similar direction along a roadway as the first vehicle; andwithin a risk zone of the first vehicle, wherein the risk zonecorresponds to an area where there is a risk of collision between thefirst vehicle and the second vehicle.

In Example 20, the instructions in any of Examples 16-19 further causethe one or more processors to determine that the state of the turnsignal indicator corresponds to a direction of movement of the firstvehicle.

Example 21 is a system or device that includes means for implementing amethod or realizing a system or apparatus in any of Examples 1-20.

It should be noted that the sensor embodiments discussed above maycomprise computer hardware, software, firmware, or any combinationthereof to perform at least a portion of their functions. For example, asensor may include computer code configured to be executed in one ormore processors, and may include hardware logic/electrical circuitrycontrolled by the computer code. These example devices are providedherein purposes of illustration, and are not intended to be limiting.Embodiments of the present disclosure may be implemented in furthertypes of devices, as would be known to persons skilled in the relevantart(s).

Embodiments of the disclosure have been directed to computer programproducts comprising such logic (e.g., in the form of software) stored onany computer useable medium. Such software, when executed in one or moredata processing devices, causes a device to operate as described herein.

While various embodiments of the present disclosure have been describedabove, it should be understood that they have been presented by way ofexample only, and not limitation. It will be apparent to persons skilledin the relevant art that various changes in form and detail can be madetherein without departing from the spirit and scope of the disclosure.Thus, the breadth and scope of the present disclosure should not belimited by any of the above-described exemplary embodiments, but shouldbe defined only in accordance with the following claims and theirequivalents. The foregoing description has been presented for thepurposes of illustration and description. It is not intended to beexhaustive or to limit the disclosure to the precise form disclosed.Many modifications and variations are possible in light of the aboveteaching. Further, it should be noted that any or all of theaforementioned alternate implementations may be used in any combinationdesired to form additional hybrid implementations of the disclosure.

Further, although specific implementations of the disclosure have beendescribed and illustrated, the disclosure is not to be limited to thespecific forms or arrangements of parts so described and illustrated.The scope of the disclosure is to be defined by the claims appendedhereto, any future claims submitted here and in different applications,and their equivalents.

1. A method comprising: detecting, at a first vehicle, a presence of asecond vehicle in an adjacent lane; identifying, in an image of thesecond vehicle, a sub-portion of the image containing a turn signalindicator of the second vehicle, wherein identifying the sub-portioncontaining the turn signal indicator comprises processing the image ofthe second vehicle using a first neural network trained to recognize oneor more turn signal regions of interest; processing the sub-portion ofthe image to determine a state of the turn signal indicator using asecond neural network trained to determine a state of one or more turnsignal indicators; and notifying a driver or performing a drivingmaneuver, at the first vehicle, based on the state of the turn signalindicator.
 2. (canceled)
 3. (canceled)
 4. The method of claim 1, furthercomprising determining the driving maneuver for the first vehicle basedon the state of the turn signal indicator.
 5. The method of claim 4,wherein determining the driving maneuver comprises providing the stateof the turn signal indicator into a lane-changing decision matrix andprocessing the lane-changing decision matrix to select the drivingmaneuver.
 6. The method of claim 4, wherein notifying the drivercomprises notifying the driver of the determined driving maneuver. 7.The method of claim 1, further comprising predicting future movement ofthe second vehicle based on a state of the turn signal indicator.
 8. Themethod of claim 7, wherein predicting future movement comprisespredicting a lane change from the adjacent lane into a same lane as thefirst vehicle.
 9. The method of claim 7, wherein notifying the drivercomprises notifying the driver of the predicted future movement of thesecond vehicle.
 10. The method of claim 1, further comprisingdetermining a direction of movement of the first vehicle correspondingto the state of the turn signal indicator.
 11. A driving control systemfor a vehicle, the system comprising: one or more sensors for obtainingsensor data in a region near a vehicle, the one or more sensorscomprising a camera; a boundary component configured to identify asub-portion of an image containing a turn signal indicator of a proximalvehicle, wherein the boundary component identifies the sub-portioncontaining the turn signal indicator by processing the image of thesecond vehicle using a first neural network trained to recognize one ormore turn signal regions of interest; a turn signal component configuredto process the sub-portion of the image to determine a state of the turnsignal indicator using a second neural network trained to determine astate of one or more turn signal indicators; and a driving maneuvercomponent configured to determine a driving maneuver for the firstvehicle based on the state of the turn signal indicator.
 12. The drivingcontrol system of claim 11, wherein the driving maneuver comprises asuggested driving maneuver, the driving control system furthercomprising a notification component configured to provide the suggesteddriving maneuver to a human driver or an automated driving system. 13.(canceled)
 14. The driving control system of claim 11, furthercomprising a detection component configured to determine that theproximal vehicle is within a risk zone with respect to the vehicle,wherein the driving control system is further configured to obtain theimage in response to the detection component determining that theproximal vehicle is within the risk zone.
 15. The driving control systemof claim 11, further comprising a detection component configured todetermine a lane of one or more of the vehicle and the proximal vehicle.16. Non-transitory computer readable storage media storing instructionsthat, when executed by one or more processors, cause the one or moreprocessors to: obtain, at a first vehicle, a plurality of images of asecond vehicle on a road near the first vehicle; identify a sub-portionof the plurality of images containing a turn signal indicator of thesecond vehicle, wherein identifying the sub-portion containing the turnsignal indicator in the plurality of images comprises processing theplurality of images using a first neural network trained to recognizeone or more turn signal regions of interest; process the sub-portion ofthe plurality of images to determine a state of the turn signalindicator using a second neural network trained to determine a state ofone or more turn signal indicators; and predict a driving maneuver forthe second vehicle based on the state of the turn signal indicator. 17.The computer readable storage of claim 16, wherein the instructionsfurther cause the one or more processors to determine a driving maneuverfor the first vehicle based on the predicted driving maneuver for thesecond vehicle.
 18. The computer readable storage of claim 16, whereinthe instructions further cause the one or more processors to determinewhether or not to cause the first vehicle to perform a lane change basedon the turn signal indicator.
 19. The computer readable storage of claim16, wherein the instructions further cause the one or more processors todetermine that the second vehicle is one or more of: in a laneimmediately adjacent a lane of the first vehicle; driving in a similardirection along a roadway as the first vehicle; and within a risk zoneof the first vehicle, wherein the risk zone corresponds to an area wherethere is a risk of collision between the first vehicle and the secondvehicle.
 20. The computer readable storage of claim 16, wherein theinstructions further cause the one or more processors to determine thatthe state of the turn signal indicator corresponds to a direction ofmovement of the first vehicle.